Office Lab 1
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Description
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This dataset contains sensor data that was collected in the office of a Professor at KU Leuven, Leuven, Belgium over a period of approximately 2 years. 
The files contain raw sensor events, no activities are annotated.

The sensor events are generated from multi-sensors, comprising a motion sensor (these files begin with "Motion" followed by the sensor number), a 
temperature sensor (these files begin with "Temperature" follewed by the sensor number), a relative humidity sensor (these files begin with "Humidity" 
followed by the sensor number) and a photo resistor measuring light intensity (these files begin with "Light" followed by the sensor number). In addition, 
a door/window closure sensor is attached to each door/window (these files begin with "Door" or "Window" followed by the sensor number). Furthermore, an 
energy meter was installed to measure the radiator’s energy consumption. The energy meter generates a pulse when 1kWh of energy is used (this information
can be found in the file named "Energy"). Finally, labeled occupancy data is provided for a period of about 1 month (this information can be 
found in the file named "Labels"). 

Measured variable        | Resolution
-------------------------------------
Motion                   | 1 minute
Temperature  	         | 1 minute
Humidity                 | 1 minute
Light intensity          | 1 minute
Door/window closure      | 20 seconds
Energy cons. of radiator | -
Occupancy labels         | 1 minute

Sensor layout
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The layout of the sensors in the office is shown in the file sensor_layout.png.

References
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Please cite one of the related articles in any publications resulting from using this dataset.

- De Bock, Y., Auquilla, A., Nowé, A., & Duflou, J. R. (2020). Nonparametric user activity modelling and prediction. User Modeling and User-Adapted 
  Interaction, 1-29.

- De Bock, Y., Auquilla, A., Kellens, K., Vandevenne, D., Nowé, A., & Duflou, J. R. (2017). User-adapting system design for improved energy 
  efficiency during the use phase of products: Case study of an occupancy-driven, self-learning thermostat. In Sustainability through innovation 
  in product life cycle design (pp. 883-898). Springer, Singapore.

- De Bock, Y., Auquilla, A., Kellens, K., Nowé, A., & Duflou, J. R. (2016, September). Intelligent occupancy-driven thermostat by dynamic user profiling. 
  In 2016 Electronics Goes Green 2016+(EGG) (pp. 1-8). IEEE.